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Data Engineering

Basics to Advanced

Course Syllabus

Data engineering is a field that involves designing, building, and maintaining systems, pipelines, and architectures to collect, store, process, and transform raw data into usable and valuable information. Data engineers work with various tools and frameworks to handle data at scale and support data-driven applications and analytics.

This comprehensive course provides an in-depth exploration of data engineering principles, methodologies, and tools essential for managing big data effectively. Participants will gain hands-on experience with various tools and techniques used in the field of data engineering, enabling them to build scalable, reliable, and efficient data & ML pipelines.

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Tools You'll Learn
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Essential Math & Statistics

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MS Power BI

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Hadoop

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Spark

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BigQuery

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Looker Studio

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Google Sheets

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Advanced Excel

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MySQL

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Beam

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Kafka

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Machine Learning

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Business Finance

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Google Workspace

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Python

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Tableau

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Airflow

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Flink

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Ansible

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Git & GitHub

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Placement Training

Our Roadmap
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Mathematics & Statistics
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A significant portion of your ability to translate your data science skills into real-world scenarios depends on your success and understanding of mathematics. Data science careers require mathematical and stattistical study because algorithms, and performing analysis and discovering insights from data require math and stats.

  • Numbers & Number System

  • Factorization of Numbers

  • Mathematical Computation

  • Number Series

  • Ratio & Proportion

  • Percentage, Percentile & IQR

  • Linear algebra

  • Hypothesis testing

  • Regression

  • MSE & RMSE

  • Covariance

  • Composite functions

  • Statistical decision theory

  • Calculus

  • ANOVA

  • Descriptive statistics

  • Profit/ Loss

  • Interest

  • Permutation and Combination

  • Set Theory

  • Probability

  • Measures of Central Tendency

  • Discrete Mathematics

  • Random variables

  • Bayes Theorem

  • Central limit theorem

  • Probability theorms

  • Conditional probability

  • Distributions

  • Sampling

  • Calculus and Optimization

  • Exponential

Advanced Excel
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(8+ Live Projects)

Excel is a spreadsheet application that can be used for data analytics. Data analysts use Excel to analyze large amounts of data quickly and easily. With its wide range of charting and graphing options, Excel can help users to present data in a way that is easy to understand. Charts and graphs are essential tools for data analysis, as they allow users to quickly identify patterns and trends in data. What you'll learn here:

  • Excel Interface

  • Data Formatting Techniques

  • Data Cleaning Techniques

  • Data Study Techniques

  • Conditional Formatting

  • Data Validation

  • Calculations in Excel

  • Operators in Excel

  • Different Types of Operators

  • Mathematical & Statistical Calculations

  • Financial Calculations

  • Basic Inbuilt Excel Functions (SUM, MIN, MAX, COUNT etc.)

  • Advanced Functions (VLOOKUP, SUMIFS, COUNTIS etc.)

  • Macros, VBA, Power Pivot & Power Query

  • Analytics Using Functions

  • Pivot Tables

  • Analytics Using Pivot Tables

  • Data Visualization in Excel

  • Applying Charts & Graphs

  • Dashboard & Report Building

Python
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(5+ Live Projects)

Python has become a popular programming language for data analysis and data science due to its simplicity, versatility, and a wide range of libraries and frameworks specifically designed for data manipulation, exploration, and visualization. It's easy to learn, widly used & versatile. It also has wide range of libraries and frameworks. What you'll learn here:

  • Introduction to Python

  • Environment Setup

  • Installing Anaconda

  • Working with Jupyter Notebooks & Lab

  • Python Basics

  • Syntax

  • Variables

  • Data Types

  • Type Casting

  • Keywords & Identifiers

  • Operators

  • Types of Operators

  • Mathematical Calculations

  • Data Structures in Python

  • Int and Float

  • Complex Numbers

  • Boolean

  • Strings

  • String Methods

  • Lists

  • Multidimentional Lists

  • List Methods

  • Tuples

  • Tuple Methods

  • Sets and Frozen Sets

  • Set Methods

  • Dictionary & Methods

  • Comprehensions

  • Functions

  • Modules

  • Libraries

  • Importing Libraries

  • Complete OOP

  • Data Analytics in Python

  • Introduction to NumPy

  • NumPy Methods

  • NumPy Data Types

  • NumPy Calculations

  • Data Manipulation with NumPy

  • Introduction to Pandas

  • Pandas Data Structures

  • Working with DataFrames

  • Importing Data File Types

  • Data Manipulations

  • Data Cleaning

  • Data Wrangling

  • Generating Insights

  • Exporting Data

  • Data Visualization with Matplotlib

  • Data Visualization with Seaborn

  • Working with Different Chart Types

  • Effective Data Visualization

Power BI
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(10+ Live Projects)

Power BI is a business intelligence service from Microsoft that helps businesses and individuals transform raw data into insights. It is a group of BI (business intelligence) services and products that converts data from various sources into reports, visualizations, and interactive dashboards. Power BI is a valuable tool for illustrating what’s happening within an organization in the present. It also has applications for helping to anticipate what may transpire in the future. What you'll learn here:

  • Introduction to Business Intelligence

  • Different BI Tools

  • Introduction to Power BI

  • Installing Power BI Desktop

  • Working with Interface

  • Understanding 4 Views

  • Report, Table, Model & Query View

  • Importing Different Data Files

  • Types of Import

  • Introduction to Big Data

  • Working with Canvas

  • Working with Charts

  • Choosing Correct Chat Types

  • Formatting Charts

  • Types of Formatting

  • Importing Visuals

  • Applying Themes

  • Building Dahboards & Reports

  • Data Tranformation

  • Data Cleaning

  • Introduction to Power Query

  • Power Query Editor

  • Importing Data in Power Query Editor

  • Bulk Import Data

  • Applying Data Cleaning Techniques

  • Introduction to Data Modeling

  • Working with Relationships

  • Cardinality & Direction

  • Types of Tables

  • Schemas & Types of Schemas

  • Creating Schemas

  • Schema Conversion

  • Introduction to Functional Programming

  • Introduction to DAX

  • Syntax

  • Data Types

  • Operators & Types of Operators

  • Keywords & Identifiers

  • Inbuilt Functions

  • Calculation Types

  • Creating Calculated Columns

  • Creating Calculated Measures

  • Creating Calculated Tables

  • Contexts

  • Types of Contexts

  • Variables and Returns

  • Scope of Variables & Calculations

  • Comments

  • Working with Inbuilt Functions

  • Types of Inbuilt Functions

  • Analyzing Data with DAX

  • Introduction to Power BI Management

  • Workspaces

  • Publishing Dashboards & Reports

  • Introduction to Data Pipelines and Pipelining

  • Introduction to Data Mining

  • Understanding ETL Process

  • Creating Data Flow

  • Working with Semantic Model

  • Connecting Data Flow to Semantic Model

  • Creating Real Time and Automated Reports

  • Building End to End Data Pipeline

  • Using SQL Queries in Power BI

  • Using Python in Power BI

MySQL
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(5+ Live Projects)

MySQL is a relational database management system (RDBMS) that's used as an interface for SQL. SQL is a structured query language (SQL) that allows you to query, define, manipulate, control, and analyze data in a database. SQL databases are essential for data analysts who work on data architecture and storage systems. MySQL is one of the most sought-after skills in any data-related job, and can boost your job prospects by over 40%. What you'll learn here:

  • Introduction to DBMS & RDBMS

  • Introduction to Structured Query Language

  • Installing MySQL

  • Working with MySQL Workbench

  • Understanding Databases

  • Database Designing

  • Keys and Types of Keys

  • Relationships and Normalization

  • MySQL Query Basics

  • MySQL Data Types

  • MySQL Functions

  • DDL(Data Definition Language)

  • DML(Data Manipulation Language)

  • DQL(Data Query Language)

  • TCL(Transaction Control Language)

  • DCL(Data Control Language)

  • MySQL SELECT

  • MySQL WHERE

  • MySQL AND, OR, NOT

  • MySQL ORDER BY

  • MySQL INSERT INTO

  • MySQL NULL Values

  • MySQL UPDATE

  • MySQL DELETE

  • MySQL LIMIT

  • MySQL MIN and MAX

  • MySQL COUNT, AVG, SUM

  • MySQL LIKE

  • MySQL Wildcards

  • MySQL IN

  • MySQL BETWEEN

  • MySQL Aliases

  • MySQL Joins

  • Sub-Queries & CTE

  • MySQL INNER JOIN

  • MySQL LEFT JOIN

  • MySQL RIGHT JOIN

  • MySQL CROSS JOIN

  • MySQL Self Join

  • MySQL UNION

  • MySQL GROUP BY

  • MySQL HAVING

  • MySQL EXISTS

  • MySQL ANY, ALL

  • MySQL INSERT SELECT

  • MySQL CASE WHEN

  • MySQL Null Functions

  • MySQL Comments

  • MySQL Operators

  • MySQL Create DB

  • MySQL Drop DB

  • MySQL Create Table

  • MySQL Drop Table

  • MySQL Alter Table

  • MySQL Constraints

  • MySQL Not Null

  • MySQL Unique

  • MySQL Primary Key

  • MySQL Foreign Key

  • MySQL Check

  • MySQL Default

  • MySQL Create Index

  • MySQL Auto Increment

  • MySQL Dates

  • MySQL Views

  • Window Functions

  • MySQL Triggers

  • MySQL Stored Procedures

Tableau
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(5+ Live Projects)

Tableau is a business intelligence tool that helps organizations analyze and process large amounts of data. Tableau is an end-to-end data analytics platform that allows you to prep, analyze, collaborate, and share your big data insights. Tableau excels in self-service visual analysis, allowing people to ask new questions of governed big data and easily share those insights across the organization. What you'll learn here:

  • Installing Tableau

  • Tableau Fundamentals

  • Tableau Desktop

  • Tableau Server

  • Tableau Online

  • Tableau Reader

  • Tableau Public

  • Connecting With Data

  • Creating Views and Analysis

  • Dashboard Designs

  • Creating Reports

  • Data Cleaning

  • LOD Expressions

  • Creating Parameters

  • Calculated Fields

  • Generating Data Stories

Looker Studio
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(3+ Live Projects)

With Looker Studio, you can easily report on data from a wide variety of sources, without programing. In just a few moments, you can connect to data sets such as: Databases, including BigQuery, MySQL, and PostgreSQL. Google Marketing Platform products, including Google Ads, Analytics, Display 360, Search Ads etc. What you'll learn here:

  • Introduction to Looker

  • Interface

  • Getting Data

  • Charts & Graphs

  • Grouping & Categorizing Data

  • Data Blending

  • Filters & Controls

  • Parameters

  • Sharing, Tracking & Management

  • BigQuery

Business Finance
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(3+ Live Projects)

Finance in data analytics is a field that applies data analysis techniques to financial data to support decision making, optimize performance, and prevent fraud. Learning finance in data analytics can help you gain valuable skills such as financial data analysis. By learning finance in data analytics, you can enhance your career prospects and opportunities in the finance industry, as well as other industries that rely on financial data. You can also improve your financial literacy and decision making for your personal or professional goals. What you'll learn here:

  • Introduction to Finance

  • Getting Familier with Financial Terms

  • Revenue, Expense,  Profit

  • Gross Revenue, Gross Profit, Net Profit

  • EBIT and EBITDA

  • MTD, QTD, YTD

  • Plan, Budget, Forecast, LE

  • General Ledger

  • Cash Flow and Income Statement

  • Balance Sheet

  • Profit and Loss Statement

  • Variance

  • Favorable and Unfavorable Variance

  • YoY, vs LY, vs Bud

  • ROI, ROE, ROA, EPS

  • P/E Ratio

  • NPV, IRR

  • AAGR & CAGR

Google Workspace
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(3+ Live Projects)

Google Sheets is a powerful tool for data analytics, offering accessibility, collaboration, cost-effectiveness, and basic data analysis capabilities. Learning it enables efficient data manipulation, visualization, and sharing, making it an essential skill for anyone handling data analysis tasks. Google Sheets provides a user-friendly interface familiar to many, facilitating a smooth learning curve. Its integration with other Google Workspace apps enhances productivity, while its cloud-based nature ensures data accessibility from anywhere. What you'll learn here:

  • Getting started with Google Sheets

  • Google Sheets Interface

  • Data Formatting

  • Data Manipulation

  • Data Cleaning

  • Formulas & Functions

  • Basic Functions (SUM, AVERAGE, COUNT etc.)

  • Advanced Functions (VLOOKUP, IF, INDEX etc.)

  • Pivot Tables

  • Introduction to Google Apps

  • Exploring Data Collection Tools

  • Presentation with Google Slides

  • Documentation with Google Doc

  • Collaboration

  • Sharing

  • Google Drive

  • Google Groups

  • Practice and Projects

Apache Hadoop
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(3+ Live Projects)

Apache Hadoop software is an open source framework that allows for the distributed storage and processing of large datasets across clusters of computers using simple programming models. Hadoop is designed to scale up from a single computer to thousands of clustered computers, with each machine offering local computation and storage. In this way, Hadoop can efficiently store and process large datasets ranging in size from gigabytes to petabytes of data. What you'll learn here:

  • Introduction to Data Engineering

  • Introdcution to Big Data

  • Overview of Apache Hadoop

  • Hadoop Distributed File System (HDFS)

  • Single-node Hadoop cluster

  • HDFS architecture

  • Namenode

  • Datanode

  •  Secondary Namenode

  • HDFS operations

  • Reading, Writing, and Deleting files

  • Data replication and fault tolerance in HDFS

  • HDFS commands and utilities

  • Working with in HDFS using Python

  • Using libraries like hdfs3 or pyarrow

  • Introduction to MapReduce

  • MapReduce programming paradigm

  • MapReduce phases

  • Map, Shuffle, and Reduce

  • Anatomy of a MapReduce job

  • Mapper, Reducer

  • InputFormat, and OutputFormat

  • MapReduce programs using mrjob & hadoopy

  • Combiners and Partitioners

  • Working with multiple inputs and outputs

  • Custom counters and status updates

  • Tuning MapReduce jobs for performance

  • Implementing MapReduce Algorithms

  • Hadoop Ecosystem

  • Hive and Pig

  • Introduction to Apache Hive

  • HiveQL for data warehousing

  • Running Hive queries to analyze data

  • Introduction to Apache Pig

  • Executing Pig scripts for data processing

  • Introduction to Apache HBase

  • HBase architecture

  • HMaster, RegionServers, and HDFS Integration

  • Interacting with HBase tables using happybase

  • Introduction to Apache Sqoop

  • Importing data using Sqoop and Python

  • Introduction to Hadoop cluster architecture

  • Master and Slave nodes

  • Cluster planning & deployment considerations

  • Monitoring and managing Hadoop clusters

  • Backup and recovery strategies

  • Managing a multi-node cluster using Python

  • Scaling and optimizing Hadoop clusters

Apache Beam
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(3+ Live Projects)

Apache Hadoop software is an open source framework that allows for the distributed storage and processing of large datasets across clusters of computers using simple programming models. Hadoop is designed to scale up from a single computer to thousands of clustered computers, with each machine offering local computation and storage. In this way, Hadoop can efficiently store and process large datasets ranging in size from gigabytes to petabytes of data. What you'll learn here:

  • Introduction to Apache Beam

  • Introduction to the Beam programming model

  • Pipelines, Transforms, and Collections

  • Setting up the development environment

  • Beam pipeline using Python

  • Concepts of Beam pipelines

  • PCollections, PTransforms, and Pipeline I/O

  • Working with different types of data sources

  • Batch vs. Streaming processing with Python

  • Mapping, Filtering, Aggregating, Joining etc.

  • Working with key-value pairs in Beam

  • Windowing, Triggering, and Watermarking

  • Event time processing & windowing

  • Managing stateful computations & side inputs

  • Timers & Triggers for time-sensitive operations

  • Runners, Connectors, & Extensions with Python

  • Beam SQL for declarative data processing

  • Parallelism, Management, & Data Locality

Apache Airflow
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(3+ Live Projects)

Apache Airflow is a workflow engine that will easily schedule and run your complex data pipelines. It will make sure that each task of your data pipeline will get executed in the correct order and each task gets the required resources. It will provide you an amazing user interface to monitor and fix any issues that may arise. What you'll learn:

  • Introduction to Workflow Orchestration

  • Introduction to Apache Airflow

  • Understanding the Airflow architecture

  • Scheduler, Executor, Metadata , & Web Server

  • Installing and configuring Apache Airflow

  • Introduction to Directed Acyclic Graphs (DAGs)

  • Defining & organizing workflows using DAGs

  • Tasks, and Operators

  • Understanding task dependencies

  • Scheduling Strategies in Airflow

  • Creating & managing workflows

  • Introduction to Airflow Operators

  • BashOperator, PythonOperator, SQLOperator

  • Types of operators, task execution & processing

  • Writing custom operators

  • Extending Airflow functionality

  • Understanding sensors in Apache Airflow

  • FileSensor, HttpSensor, ExternalTaskSensor, etc.

  • Working with sensors to trigger workflows

  • Implementing dynamic workflows

  • Conditional task execution in Airflow

  • Implementing data sensors and triggers

  • Managing dependencies in tasks & workflows

  • Defining task dependencies

  • Implementing error handling & retry policies

  • Introduction to advanced Airflow features

  • XCom, Variables, Hooks, etc.

  • Implementing alerts and notifications

  • Monitoring and logging workflow

  •  Designing scalable & maintainable workflows

Machine Learning
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(5+ Live Projects)

Data is meaningless until it's converted into valuable information. Machine learning can be used as the key to unlock the value of corporate and customer data and enact decisions that keep a company ahead of the competition. Machine learning and Data Science are hence two sides of a coin without which Data Science operations are unachievable. Data Scientists must grasp Machine Learning knowledge for accurate forecasts and estimates. What you'll learn here:

  • Introduction to Machine Learning

  • Essential Maths for Machine Learning

  • Essential Statistics for Machine Learning

  • Exploratory Data Analysis (EDA)

  • Hypothesis Testing

  • t-tests and chi-square tests

  • Using Python for Machine Learning

  • Important Python Libraries for ML

  • Supervised Machine Learning

  • Unsupervised Machine Learning

  • Reinsforcement Machine Learning

  • Regression Analysis

  • Multi-Regression Analysis

  • Principal Component Analysis

  • Clustering

  • K-Means Clustering

  • Dimentionality Reduction

  • Classification

  • Support Vector Machine

  • K-Nearest Neighbours

  • Random Forest

  • Decision Tree

  • Ensemble Learning

  • Confusion Matrix

  • Model Fine Tuning

  • Model Over-Fitting

  • Model Under-Fitting

  • Training, Testing & Deployment

Apache Spark
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(3+ Live Projects)

Apache Spark is an open-source, distributed processing system used for big data workloads. It utilizes in-memory caching, and optimized query execution for fast analytic queries against data of any size. It provides development APIs in Java, Scala, Python and R, and supports code reuse across multiple workloads—batch processing, interactive queries, real-time analytics, machine learning, and graph processing. You’ll find it used by organizations from any industry, including at FINRA, Yelp, Zillow, DataXu, Urban Institute, and CrowdStrike. What you'll learn:

  • Introduction to  Apache Spark

  • Understanding the Spark architecture

  • RDDs, Transformations, and Actions

  • Writing and running a Spark application

  • Exploring the Spark DataFrame API & Spark SQL

  • Working with structured data

  • Reading, Writing, & Manipulating DataFrames

  • Understanding lazy evaluation

  • Understanding Spark's execution model

  • Performing data manipulation & analysis

  • Spark transformations and actions

  • Working with RDDs and Pair RDDs

  •  map, filter, reduceByKey, and join operations

  • Introduction to Spark SQL functions

  • User-Defined Functions (UDFs)

  • Caching, Persistence, and Checkpointing

  • Understanding Spark streaming

  • DStreams and structured streaming

  • Machine Learning with Spark MLlib

  • Building & training machine learning models

  • Working with different data sources

  • HDFS, HBase, Hive, JDBC, and Parquet

  • Spark performance bottlenecks

  • Optimization Techniques

  • Management, Parallelism, & Tuning Parameters

  • Monitoring, Debugging, and Troubleshooting

  • ML and Stream Processing

  • ETL Pipelines

Apache Kafka
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(3+ Live Projects)

Apache Kafka is a distributed data streaming platform that allows users to store, process, and publish streams of records in real-time. It's an open-source platform that combines messaging, storage, and stream processing to store and analyze data. Apache Kafka allows fast communication between different servers and users, offering players a low-latency experience. The real-time event streaming capabilities benefit analytics and machine learning applications. What you'll learn:

  • Introduction to Streaming Data

  • Introduction to Apache Kafka

  • Understanding the Kafka architecture

  • Installing and setting up Apache Kafka

  • Setting up a single-node Kafka cluster

  • Exploring Kafka core concepts

  • producers, consumers, and brokers

  • partitions, and replication

  • Kafka message format and serialization

  • brokers, ZooKeeper, and controller

  • Kafka client APIs with Python

  • Producer, Consumer, and AdminClient API

  • Kafka architecture

  • brokers, ZooKeeper, and controller

  • Configuring & managing Kafka clusters

  • Kafka storage internals

  • logs, segments, and retention policies

  • Deploying & configuring Kafka cluster

  •  Kafka Streams API for stream processing

  • Stream processing concepts

  • Stateful and Stateless operations

  • Building stream processing applications

  • Kafka Connect

  • Kafka Connect concepts

  • Connectors, Tasks, and Worker Nodes

  • Using Kafka Connect for data ingestion

  • Integration with external systems

  • Monitoring and managing Kafka clusters

Apache Flink
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(3+ Live Projects)

Apache Flink is an open-source, distributed engine for stateful processing over unbounded (streams) and bounded (batches) data sets. Stream processing applications are designed to run continuously, with minimal downtime, and process data as it is ingested. Data streaming is one of the most exciting areas of enterprise technology today, and stream processing with Flink makes it even more powerful. Learning Flink will be beneficial for your career, because real-time data processing is becoming more valuable to businesses globally. What you'll learn

  • Introduction to Apache Flink

  • Flink architecture

  • Data streams, Transformations, and State

  • Setting up a Flink development environment

  • Flink DataStream API 

  • DataStream transformations

  • map, filter, window, and join operations

  • Event time processing

  • Watermarks in Flink

  • Building & executing stream processing apps

  • Flink Table API for relational stream processing

  • Table API concepts

  • Tables, SQL queries, and Data Types

  • Working with tables & views in Flink

  • Writing SQL queries using Flink API

  • Relational stream processing tasks

  • Stateful stream processing in Flink

  • Flink's State management

  • Keyed State and Operator State

  • Stateful Transformations and Time Windows

  • Stateful Stream Processing Application

  • Introduction to Flink SQL

  • Writing complex event processing (CEP)

  • Pattern Detection & Event-Driven Architecture

  • Flink connectors

  • Working with various data sources

  • Flink's integration with other systems

  • Flink's windowing and session windows

  • Fault Tolerance and High Availability in Flink

  • Performance optimization in Flink

Google BigQuery
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(3+ Live Projects)

BigQuery is a fully managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence. BigQuery's serverless architecture lets you use SQL queries to answer your organization's biggest questions with zero infrastructure management. Federated queries let you read data from external sources while streaming supports continuous data updates. BigQuery's scalable, distributed analysis engine lets you query terabytes in seconds and petabytes in minutes. What you'll learn:

  • Introduction to Google BigQuery

  • Architecture of BigQuery

  • Storage, Compute, and Execution Model

  •  BigQuery Python client library

  • Connecting to BigQuery using Python

  • Data Ingestion methods

  • Batch and Streaming

  • Python for data import/export

  • Google Cloud Storage (GCS)

  • Loading data into BigQuery

  • Exporting data from BigQuery to GCS

  • SQL querying in BigQuery

  • Subqueries, Joins, and Window Functions

  • Data Analysis and Visualization

  • Introduction to BigQuery ML

  • Supervised and Unsupervised Learning

  • Feature Engineering

  • Building & training ML models

  • Advanced ML models in BigQuery ML

  • XGBoost & TensorFlow

  • Model Evaluation & Performance Metrics

  • Fine-tuning and optimization

  • Data Modeling

  • Schema Design

  • Partitioning in BigQuery

  • Access control, encryption, & Data Governance

Ansible
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(3+ Live Projects)

BigQuery is a fully managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence. BigQuery's serverless architecture lets you use SQL queries to answer your organization's biggest questions with zero infrastructure management. Federated queries let you read data from external sources while streaming supports continuous data updates. BigQuery's scalable, distributed analysis engine lets you query terabytes in seconds and petabytes in minutes. What you'll learn:

  • Introduction to Ansible

  • Understanding Ansible Architecture

  • Installation & setup of Ansible on a control node

  • Writing & executing Ansible ad-hoc commands

  • Installing Ansible & Configuring Inventory

  • Introduction to Ansible Playbooks

  • Structure, Syntax, and YAML Format

  • Organizing Tasks into Playbooks

  • Defining Playbook Variables

  • Reusable Components for Organizing Tasks

  • Ansible roles for data engineering tasks

  • Infrastructure as code (IaC)

  • Ansible for infrastructure provisioning

  • Dynamic inventory management with Ansible

  • Automating infrastructure provisioning tasks

  • Introduction to configuration management

  • Packages on managed nodes using Ansible

  • Ansible modules for package management

  • File Operations, and System Configuration

  • Data Pipeline Orchestration

  • Ansible for deploying & orchestrating

  • Integrating Ansible with other frameworks

Placement Training &
Interpersonal Skills
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Interpersonal skills, also called soft skills or people skills, are important for data analysts because they help with many aspects of their work. Effective communication is essential for data analysts to effectively convey their findings, recommendations, and insights to their audiences. Also some companies do conduct aptitude tests for data analytics jobs. To prepare for such a test, you can focus on improving your problem-solving and critical thinking skills. If you're a data analyst searching for your next career opportunity, one of the most critical components of your job search is your resume or CV. A data analyst resume is your first chance to make an impression to potential employers, and it needs to stand out from the rest to land an interview. What you'll learn here:

  • Effective Communication

  • Reasoning & Aptitude

  • HR Prep

  • Techincal Interview Prep

  • Presentation Skills

  • Personality Development

  • GitHub  & LinkedIn Profiles

  • Mock Interview Sessions

  • Resume Making

  • Applying on Job Boards

Job Profiles
You Can Target
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Job Profiles

With Average Salary for Freshers

Data Engineer

Average Salary

7-12 LPA

Big Data Eng.

Average Salary

14-20 LPA

Warehouse Eng.

Average Salary

8-15 LPA

ML Data Engineer

Average Salary

18-25 LPA

Big Data Scientist

Average Salary

14-18 LPA

BI Developer

Average Salary

17-26 LPA

Our Approach Towards Teaching
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From Basics to Advanced

Learn With Ease

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Embark on your learning journey from the basics with industry experts. Starting with familiar tools like Excel and fundamental math concepts

Take on Challenges

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Learning technology is like learning swimming - you can truly learn it only when you practice. After each subject, dive into challenging projects that let you apply what you've learned.

Active Engagement

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Stay connected through dedicated WhatsApp groups where you can post doubts—our vibrant community of students and faculty is there to provide quick solutions

Job-Ready Training by IITians​​

Prepare for success with our placement training led by industry experts. Dive into resume building, tackle puzzles and aptitude challenges, refine your communication skills, excel in mock interviews, engage in group discussions, and much more.

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